More payers are using statistical sampling and extrapolation to extract payments from labs

CEO SUMMARY: When commercial and government payers use auditors to review a lab’s claims, they often use statistical sampling and extrapolation to limit the time needed to review claims. But proper sampling and extrapolation require following the rigorous scientific methods to produce a representative sample of claims to draw conclusions they can apply to the universe of claims. A lawyer involved in such cases cautions that payers cannot simply plug in numbers to produce a result in their favor.

THREE YEARS AGO, federal auditors cited at least three genetic testing lab companies under federal law, saying the labs had filed false claims totaling multiple millions of dollars for molecular and genetic tests.

In each case, the lab companies protested that federal auditors unfairly used statistical sampling to identify a small number of potentially fraudulent claims and then, from those numbers, the auditors extrapolated a much larger number that reflected the actual amount the labs owed.

In these cases, the use of statistical sampling and extrapolation was damaging and costly to the lab companies involved. Now, a lawyer who often works with labs in these cases has cautioned clinical, genetic, and molecular testing laboratories that all auditors must follow proper procedures when using statistical sampling and extrapolation to produce accurate audit results.

“The key to defending against any false claims charges that stem from an auditor’s use of statistical sampling and extrapolation is to understand the mathematical and scientific principles behind these techniques,” advised Jeffrey J. Sherrin, a lawyer who represents labs in these cases. Sherrin is President of O’Connell & Aronowitz in Albany, N.Y.

Is Sampling Done Correctly?

“The use of statistical sampling and extrapolation in government overpayment audits is well accepted,” Sherrin explained in an interview with THE DARK REPORT. “It is a given that the government can do this. The question becomes whether they do it correctly. That’s where labs can make their case.

“In my work with labs, I have been very involved in cases where auditors have used statistical sampling and extrapolation,” he added. “Actually, both have come up in several contexts. Typically, sampling and extrapolation come up in Medicare and Medicaid overpayment audits.

“In those cases, the auditors will look at whether you crossed the ‘t’ or dotted the ‘i’, and if you didn’t, they take those examples and calculate how much your lab owes,” stated Sherrin.

“Increasingly, the use of statistical sampling and extrapolation is growing,” he warned. “Commercial payers are using these techniques and the government is using them in false claims cases to prove either liability or damages or both.

“Among commercial payers, it is not well established that they can use statistical sampling and extrapolation,” Sherrin commented. “Instead, the use of these techniques is a subject of debate among lawyers and others.

“The reasoning behind the debates is this: If it’s just an audit case that’s not related to criminality, then the burden of proof is on the plaintiff, meaning the person who says you owe the money,” he explained. “In these cases, the plaintiff normally has to prove that your lab breached one or more contracts.

“Sampling and extrapolation are allowed for government audits because the process is more efficient and less costly,” Sherrin explained. “Using these techniques, the Medicare and Medicaid programs have a legitimate interest in reducing the cost to determine how much money is owed so that the cost of carrying out this government function isn’t prohibitive.

An Open Question for Payers

“If an auditor were to review 100% of, let’s say, 10,000 claims, you’ll know exactly how much is owed,” he added. “But, the cost of reviewing 10,000 cases would be prohibitive. On the other hand, if you review 1% of 10,000 claims, then you’re using statistical theory to arrive at a number that will approximately equate to what you would get if you reviewed 100% of the cases.

“That’s why commercial payers don’t necessarily have the same public policy requirement to be as inexpensive and cost efficient when going after a provider,” he added. “In some cases, lawyers will accept the idea that commercial payers can use sampling, and in other cases, lawyers will argue they can’t.

“While it’s still an open question, commercial payers are increasingly moving in that direction,” he added. “That means it’s an important issue for all providers, including laboratories.

“When a government or commercial payer uses statistical sampling and extrapolation in any false claims case, the issue of intent comes into play,” he explained. ‘The payer has to prove unlawful intent and that raises the question of whether an auditor can use sampling and extrapolation to prove intent. It’s not yet clear whether the courts will allow it or not.

Labs Suffered When Auditors Used Sampling, Extrapolation

IN RECENT YEARS, at least three pharmacogenomics testing laboratories reported that federal auditors targeted their company’s labs with audits and multi-million dollar recoupment demands.

In these cases, auditors from the federal Zone Integrity Program Contract (ZIPC) identified a small number of claims as being paid improperly and rejected those claims. Then, the auditors extrapolated from that small number of rejections to all claims filed over a period of years. The result was that the auditors demanded that each targeted lab company pay tens of millions of dollars. (See TDRs, Jan. 9 and 30, 2017, and April 3, 2017.)

In 2016, Pharmacogenetics Diagnostic Laboratory (PGxL), of Louisville, Ky., was forced to file for bankruptcy protection after such an audit of its Medicare claims.

Courts Are Split

“Because some courts will allow it and other courts do not, eventually it might get to the U.S. Supreme Court,” he predicted. “In the meantime, that battle is being fought a little bit more all the time.

“If we assume that statistical sampling and extrapolation are allowed, then you get into the difficult questions as to whether it was done properly, and there is no single way of doing it correctly,” Sherrin explained. “Since there are many ways to do it, lawyers representing labs need to ask if the result is reliable.

“Reliability of the data will depend on the total number of claims in question and the number of claims used to review,” noted Sherrin. “These two numbers create a lot of litigation for this reason: the bigger the number of samples, the more precise (or less error) there will be, or should be, in the projections.

“To understand how this works, consider how a Gallup poll will produce a result showing an error rate of, say, plus or minus 2%,” he said. “That error rate reflects a statistically-sound confidence level. In other words, Gallup will say it used statistical methods to reach that level of confidence.

Confidence in Results

“With statistical sampling and extrapolation, the commercial or government payer will need to show that it has confidence in its results,” Sherrin said. “Auditors have to arrive at a number that represents their level of confidence, and they usually use 95% as the confidence level. Typically, they derive that number from a 100-case sample. Depending on the type of audit, they actually might require many more than 100 cases.

“But many commercial payers do not use 100 cases at all,” he added. “Instead, they might use 20 cases. So, the first thing a lab’s lawyer should look at is how many cases are in the sample. In a case where the auditor uses five cases rather than, say 1,000 cases, then the margin of error will be huge.

“Next, the lab’s lawyer needs to know how the claims were chosen for review,” he said. “The auditors might say the cases are chosen randomly. However, that doesn’t mean the auditors put all the numbers in a hat and picked some arbitrary number.

“Auditors should use computer programs and algorithms that have been chosen are, in fact, random,” he advised. “If the numbers are not random, and the auditor can’t prove they’re random, then those numbers have no value whatsoever.

“For this discussion, let’s say the numbers are random and the auditor has a sample of 10, 100, or 10,000 claims,” he added. “Whatever number they choose will affect the confidence level. A low number will reflect a low-level of confidence and a higher number will reflect a higher level of confidence.

“If the lab accepts the lower number, then the risk of doing it wrong is on the auditor and not on the lab,” he commented. “But if the parties choose the higher number, then the risk is on the lab and not on the auditor.

“The point is that in every case of statistical sampling and extrapolation, there are certain conditions that auditors must meet statistically to accept the low point,” he said. “Yet, in many cases no one considers these conditions.

“Instead, the parties argue about whether the sample was big enough or small enough or whether it’s okay to sample or not sample,” he warned. “They’re not looking at whether the auditors met the conditions that they need to meet.

Eliminating Sample Bias

“Auditors have to look at whether the sample itself is large enough to at least trigger the appropriateness of using the sampling method,” he concluded. “Also, the claims in the sample have to represent the claims in the universe, meaning there should be no bias in the statistical sampling. The sample has to provide a real picture of what the universe would be.

“If auditors don’t follow these parameters, then they’re not doing the sampling or extrapolation correctly,” he said. “If that happens, then the lab in question can needlessly suffer a great loss.”